In [2]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,recall_score,precision_score
In [3]:
pd.read_csv("../input/VariableDefinitions.csv")
Out[3]:
| Variable Definitions | Unnamed: 1 | |
|---|---|---|
| 0 | country | Country interviewee is in. |
| 1 | year | Year survey was done in. |
| 2 | uniqueid | Unique identifier for each interviewee |
| 3 | location_type | Type of location: Rural, Urban |
| 4 | cellphone_access | If interviewee has access to a cellphone: Yes, No |
| 5 | household_size | Number of people living in one house |
| 6 | age_of_respondent | The age of the interviewee |
| 7 | gender_of_respondent | Gender of interviewee: Male, Female |
| 8 | relationship_with_head | The interviewee’s relationship with the head o... |
| 9 | marital_status | The martial status of the interviewee: Married... |
| 10 | education_level | Highest level of education: No formal educatio... |
| 11 | job_type | Type of job interviewee has: Farming and Fishi... |
In [4]:
train = pd.read_csv("../input/Train_v2.csv")
test = pd.read_csv("../input/Test_v2.csv")
In [5]:
train.head()
Out[5]:
| country | year | uniqueid | bank_account | location_type | cellphone_access | household_size | age_of_respondent | gender_of_respondent | relationship_with_head | marital_status | education_level | job_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Kenya | 2018 | uniqueid_1 | Yes | Rural | Yes | 3 | 24 | Female | Spouse | Married/Living together | Secondary education | Self employed |
| 1 | Kenya | 2018 | uniqueid_2 | No | Rural | No | 5 | 70 | Female | Head of Household | Widowed | No formal education | Government Dependent |
| 2 | Kenya | 2018 | uniqueid_3 | Yes | Urban | Yes | 5 | 26 | Male | Other relative | Single/Never Married | Vocational/Specialised training | Self employed |
| 3 | Kenya | 2018 | uniqueid_4 | No | Rural | Yes | 5 | 34 | Female | Head of Household | Married/Living together | Primary education | Formally employed Private |
| 4 | Kenya | 2018 | uniqueid_5 | No | Urban | No | 8 | 26 | Male | Child | Single/Never Married | Primary education | Informally employed |
In [6]:
train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 23524 entries, 0 to 23523 Data columns (total 13 columns): country 23524 non-null object year 23524 non-null int64 uniqueid 23524 non-null object bank_account 23524 non-null object location_type 23524 non-null object cellphone_access 23524 non-null object household_size 23524 non-null int64 age_of_respondent 23524 non-null int64 gender_of_respondent 23524 non-null object relationship_with_head 23524 non-null object marital_status 23524 non-null object education_level 23524 non-null object job_type 23524 non-null object dtypes: int64(3), object(10) memory usage: 2.3+ MB
In [7]:
train.describe()
Out[7]:
| year | household_size | age_of_respondent | |
|---|---|---|---|
| count | 23524.000000 | 23524.000000 | 23524.000000 |
| mean | 2016.975939 | 3.797483 | 38.805220 |
| std | 0.847371 | 2.227613 | 16.520569 |
| min | 2016.000000 | 1.000000 | 16.000000 |
| 25% | 2016.000000 | 2.000000 | 26.000000 |
| 50% | 2017.000000 | 3.000000 | 35.000000 |
| 75% | 2018.000000 | 5.000000 | 49.000000 |
| max | 2018.000000 | 21.000000 | 100.000000 |
In [8]:
test.describe()
Out[8]:
| year | household_size | age_of_respondent | |
|---|---|---|---|
| count | 10086.000000 | 10086.000000 | 10086.000000 |
| mean | 2016.975907 | 3.778802 | 38.308348 |
| std | 0.847356 | 2.212721 | 16.270053 |
| min | 2016.000000 | 1.000000 | 16.000000 |
| 25% | 2016.000000 | 2.000000 | 26.000000 |
| 50% | 2017.000000 | 3.000000 | 35.000000 |
| 75% | 2018.000000 | 5.000000 | 48.000000 |
| max | 2018.000000 | 20.000000 | 100.000000 |
In [9]:
train.country.unique()
Out[9]:
array(['Kenya', 'Rwanda', 'Tanzania', 'Uganda'], dtype=object)
In [10]:
train.groupby(["country","bank_account"])["year"].count().plot(kind="bar")
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9960ade48>
In [11]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["country"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["country"])["year"].count().plot(kind="bar",ax=ax2)
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc995fbe4a8>
In [12]:
train.year.unique()
Out[12]:
array([2018, 2016, 2017])
In [13]:
train.uniqueid.nunique()
Out[13]:
8735
In [14]:
train.groupby("country")["uniqueid"].nunique().sum()
Out[14]:
23524
In [15]:
train.bank_account.unique()
Out[15]:
array(['Yes', 'No'], dtype=object)
In [16]:
train.location_type.unique()
Out[16]:
array(['Rural', 'Urban'], dtype=object)
In [17]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["location_type"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["location_type"])["year"].count().plot(kind="bar",ax=ax2)
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc995ef1ac8>
In [18]:
(train.groupby(["location_type","bank_account"])["year"].count() / train.groupby(["location_type"])["year"].count()).plot(kind="bar")
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc995dec0b8>
In [19]:
train.cellphone_access.unique()
Out[19]:
array(['Yes', 'No'], dtype=object)
In [20]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["cellphone_access"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["cellphone_access"])["year"].count().plot(kind="bar",ax=ax2)
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9976fabe0>
In [21]:
(train.groupby(["cellphone_access","bank_account"])["year"].count() / train.groupby(["cellphone_access"])["year"].count()).plot(kind="bar")
Out[21]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc99557cb70>
In [22]:
train.household_size.unique()
Out[22]:
array([ 3, 5, 8, 7, 1, 6, 4, 10, 2, 11, 9, 12, 16, 15, 13, 14, 21,
18, 17, 20])
In [23]:
test.household_size.unique()
Out[23]:
array([ 3, 7, 6, 4, 1, 5, 2, 9, 10, 11, 8, 12, 13, 16, 15, 14, 18,
20])
In [24]:
train.sort_values(by="household_size").groupby(["household_size"])["country"].count().plot(kind="bar")
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc99554db00>
In [25]:
test.sort_values(by="household_size").groupby(["household_size"])["country"].count().plot(kind="bar")
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc993be5b70>
In [26]:
train.sort_values(by="household_size").groupby(["household_size","bank_account"])["country"].count().plot(kind="bar")
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc993aab240>
In [27]:
plt.boxplot(train.household_size)
Out[27]:
{'whiskers': [<matplotlib.lines.Line2D at 0x7fc9939bbe80>,
<matplotlib.lines.Line2D at 0x7fc9939c6208>],
'caps': [<matplotlib.lines.Line2D at 0x7fc9939c6550>,
<matplotlib.lines.Line2D at 0x7fc9939c6898>],
'boxes': [<matplotlib.lines.Line2D at 0x7fc9939bbd30>],
'medians': [<matplotlib.lines.Line2D at 0x7fc9939c6be0>],
'fliers': [<matplotlib.lines.Line2D at 0x7fc9939c6f28>],
'means': []}
In [28]:
train.age_of_respondent.unique()
Out[28]:
array([ 24, 70, 26, 34, 32, 42, 54, 76, 40, 69, 64, 31, 38,
47, 27, 48, 25, 21, 18, 22, 58, 55, 62, 29, 35, 45,
67, 19, 80, 66, 50, 33, 28, 51, 16, 17, 30, 37, 59,
65, 46, 56, 52, 23, 43, 49, 44, 72, 53, 63, 39, 81,
78, 36, 20, 60, 95, 71, 57, 85, 68, 41, 61, 75, 86,
73, 93, 74, 88, 90, 77, 84, 82, 89, 79, 83, 94, 87,
92, 91, 98, 97, 96, 99, 100])
In [29]:
train.sort_values(by="age_of_respondent").groupby(["age_of_respondent"])["country"].count().plot(kind="bar")
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc993977828>
In [30]:
plt.boxplot(train.age_of_respondent)
Out[30]:
{'whiskers': [<matplotlib.lines.Line2D at 0x7fc993718ef0>,
<matplotlib.lines.Line2D at 0x7fc9937292b0>],
'caps': [<matplotlib.lines.Line2D at 0x7fc9937295f8>,
<matplotlib.lines.Line2D at 0x7fc993729940>],
'boxes': [<matplotlib.lines.Line2D at 0x7fc993718da0>],
'medians': [<matplotlib.lines.Line2D at 0x7fc993729c88>],
'fliers': [<matplotlib.lines.Line2D at 0x7fc993729fd0>],
'means': []}
In [31]:
train.gender_of_respondent.unique()
Out[31]:
array(['Female', 'Male'], dtype=object)
In [32]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["gender_of_respondent"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["gender_of_respondent"])["year"].count().plot(kind="bar",ax=ax2)
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9936f4f28>
In [33]:
(train.groupby(["gender_of_respondent","bank_account"])["country"].count() / train.groupby(["gender_of_respondent"])["year"].count()).plot(kind="bar")
Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc99374c710>
In [34]:
train.relationship_with_head.unique()
Out[34]:
array(['Spouse', 'Head of Household', 'Other relative', 'Child', 'Parent',
'Other non-relatives'], dtype=object)
In [35]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["relationship_with_head"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["relationship_with_head"])["year"].count().plot(kind="bar",ax=ax2)
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc993776a58>
In [36]:
train.groupby(["relationship_with_head","bank_account"])["country"].count()
Out[36]:
relationship_with_head bank_account
Child No 2036
Yes 193
Head of Household No 10558
Yes 2273
Other non-relatives No 170
Yes 20
Other relative No 602
Yes 66
Parent No 1021
Yes 65
Spouse No 5825
Yes 695
Name: country, dtype: int64
In [37]:
train.groupby(["relationship_with_head","bank_account"])["country"].count() / train.groupby(["relationship_with_head"])["country"].count()
Out[37]:
relationship_with_head bank_account
Child No 0.913414
Yes 0.086586
Head of Household No 0.822851
Yes 0.177149
Other non-relatives No 0.894737
Yes 0.105263
Other relative No 0.901198
Yes 0.098802
Parent No 0.940147
Yes 0.059853
Spouse No 0.893405
Yes 0.106595
Name: country, dtype: float64
In [38]:
train.marital_status.unique()
Out[38]:
array(['Married/Living together', 'Widowed', 'Single/Never Married',
'Divorced/Seperated', 'Dont know'], dtype=object)
In [39]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["marital_status"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["marital_status"])["year"].count().plot(kind="bar",ax=ax2)
Out[39]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc99351da58>
In [40]:
train.groupby(["marital_status","bank_account"])["country"].count()
Out[40]:
marital_status bank_account
Divorced/Seperated No 1842
Yes 234
Dont know No 6
Yes 2
Married/Living together No 8883
Yes 1866
Single/Never Married No 7017
Yes 966
Widowed No 2464
Yes 244
Name: country, dtype: int64
In [41]:
train.education_level.unique()
Out[41]:
array(['Secondary education', 'No formal education',
'Vocational/Specialised training', 'Primary education',
'Tertiary education', 'Other/Dont know/RTA'], dtype=object)
In [42]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["education_level"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["education_level"])["year"].count().plot(kind="bar",ax=ax2)
Out[42]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9934cdd68>
In [43]:
train.groupby(["education_level","bank_account"])["country"].count() / train.groupby(["education_level"])["country"].count()
Out[43]:
education_level bank_account
No formal education No 0.961019
Yes 0.038981
Other/Dont know/RTA No 0.685714
Yes 0.314286
Primary education No 0.914549
Yes 0.085451
Secondary education No 0.767227
Yes 0.232773
Tertiary education No 0.489196
Yes 0.510804
Vocational/Specialised training No 0.429639
Yes 0.570361
Name: country, dtype: float64
In [ ]:
In [44]:
train.job_type.unique()
Out[44]:
array(['Self employed', 'Government Dependent',
'Formally employed Private', 'Informally employed',
'Formally employed Government', 'Farming and Fishing',
'Remittance Dependent', 'Other Income',
'Dont Know/Refuse to answer', 'No Income'], dtype=object)
In [45]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby(["job_type"])["year"].count().plot(kind="bar",ax=ax1)
test.groupby(["job_type"])["year"].count().plot(kind="bar",ax=ax2)
Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9934087f0>
In [46]:
train.groupby(["job_type","bank_account"])["country"].count() / train.groupby(["job_type"])["country"].count()
Out[46]:
job_type bank_account
Dont Know/Refuse to answer No 0.888889
Yes 0.111111
Farming and Fishing No 0.883294
Yes 0.116706
Formally employed Government No 0.224806
Yes 0.775194
Formally employed Private No 0.458768
Yes 0.541232
Government Dependent No 0.797571
Yes 0.202429
Informally employed No 0.920493
Yes 0.079507
No Income No 0.979266
Yes 0.020734
Other Income No 0.818519
Yes 0.181481
Remittance Dependent No 0.905026
Yes 0.094974
Self employed No 0.868262
Yes 0.131738
Name: country, dtype: float64
In [ ]:
In [ ]:
In [47]:
train = pd.read_csv("../input/Train_v2.csv")
test = pd.read_csv("../input/Test_v2.csv")
In [48]:
train["age_cut"] = pd.cut(train.age_of_respondent,[1,25,35,45,60,70,101],labels=[1,2,3,4,5,6])
In [49]:
train.drop("age_of_respondent",axis=1,inplace=True)
In [50]:
test["age_cut"] = pd.cut(test.age_of_respondent,[1,25,35,45,60,70,101],labels=[1,2,3,4,5,6])
In [51]:
test.drop("age_of_respondent",axis=1,inplace=True)
In [52]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby("age_cut")["country"].count().plot(kind="bar",ax=ax1)
test.groupby("age_cut")["country"].count().plot(kind="bar",ax=ax2)
Out[52]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9962dcb38>
In [53]:
train["household_cut"] = pd.cut(train.household_size,[0,3,6,40],labels=[1,2,3])
In [54]:
train.drop("household_size",axis=1,inplace=True)
In [55]:
test["household_cut"] = pd.cut(test.household_size,[0,3,6,40],labels=[1,2,3])
In [56]:
test.drop("household_size",axis=1,inplace=True)
In [57]:
fig,(ax1,ax2) = plt.subplots(1,2)
train.groupby("household_cut")["country"].count().plot(kind="bar",ax=ax1)
test.groupby("household_cut")["country"].count().plot(kind="bar",ax=ax2)
Out[57]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9932acdd8>
In [58]:
train["relationship_cut"] = train.relationship_with_head.apply(lambda x : "1" if (x=="Head of Household"
or x =="Spouse") else "0")
test["relationship_cut"] = test.relationship_with_head.apply(lambda x : "1" if (x=="Head of Household"
or x =="Spouse") else "0")
In [59]:
train["education_cut"] = train.education_level.apply(lambda x : "1" if (x=="Secondary education"
or x =="Tertiary education"
or x=="Vocational/Specialised training")
else "0")
test["education_cut"] = test.education_level.apply(lambda x : "1" if (x=="Secondary education"
or x =="Tertiary education"
or x=="Vocational/Specialised training")
else "0")
In [60]:
train["job_cut"] = train.job_type.apply(lambda x : "1" if (x=="Formally employed Government"
or x =="Formally employed Private") else "0")
test["job_cut"] = test.job_type.apply(lambda x : "1" if (x=="Formally employed Government"
or x =="Formally employed Private") else "0")
In [61]:
train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 23524 entries, 0 to 23523 Data columns (total 16 columns): country 23524 non-null object year 23524 non-null int64 uniqueid 23524 non-null object bank_account 23524 non-null object location_type 23524 non-null object cellphone_access 23524 non-null object gender_of_respondent 23524 non-null object relationship_with_head 23524 non-null object marital_status 23524 non-null object education_level 23524 non-null object job_type 23524 non-null object age_cut 23524 non-null category household_cut 23524 non-null category relationship_cut 23524 non-null object education_cut 23524 non-null object job_cut 23524 non-null object dtypes: category(2), int64(1), object(13) memory usage: 2.6+ MB
In [62]:
train.columns
Out[62]:
Index(['country', 'year', 'uniqueid', 'bank_account', 'location_type',
'cellphone_access', 'gender_of_respondent', 'relationship_with_head',
'marital_status', 'education_level', 'job_type', 'age_cut',
'household_cut', 'relationship_cut', 'education_cut', 'job_cut'],
dtype='object')
In [63]:
id_t = train.uniqueid
X_t = train.drop(["uniqueid","bank_account"],axis=1)
y_t = train.bank_account
strat = ['bank_account', 'location_type',
'cellphone_access', 'gender_of_respondent',
'marital_status','job_cut', 'age_cut',
'household_cut', 'relationship_cut', 'education_cut']
#strat = ["country","bank_account","education_cut","job_cut"]
In [64]:
train["strat"] = train.bank_account.astype(str) +"_"+train.location_type.astype(str)+"_"+train.cellphone_access.astype(str) + "_"+train.gender_of_respondent.astype(str)+"_"+train.marital_status.astype(str) + "_" + train.job_cut.astype(str) + "_" +train.age_cut.astype(str)+"_"+train.household_cut.astype(str)+"_"+train.relationship_cut.astype(str)+"_"+train.education_cut.astype(str)
In [66]:
ss = train.groupby(train.strat).count().sort_values(by="year").reset_index()
ss = ss[ss["country"] == 1]
ss["strat_1"] = ss.strat.apply(lambda x: x.split("_")[0])
In [67]:
ss = ss[["strat","strat_1"]]
In [68]:
ss.index = ss["strat"]
In [69]:
ss = ss.drop("strat",axis=1)
In [70]:
train["strat_1"] = train.strat.map(ss.strat_1)
In [71]:
train.loc[train.strat_1.isnull()==False,"strat"] = train[train.strat_1.isnull()==False]["strat_1"]
In [ ]:
In [ ]:
In [72]:
id_t = train.uniqueid
X_t = train.astype("category").drop(["uniqueid","bank_account","strat","strat_1"],axis=1)
y_t = train.astype("category").bank_account
In [73]:
X_t.columns
Out[73]:
Index(['country', 'year', 'location_type', 'cellphone_access',
'gender_of_respondent', 'relationship_with_head', 'marital_status',
'education_level', 'job_type', 'age_cut', 'household_cut',
'relationship_cut', 'education_cut', 'job_cut'],
dtype='object')
In [75]:
X_train,X_test,y_train,y_test = train_test_split(X_t,y_t,train_size=0.65,stratify=train["strat"]
,random_state=0)
In [76]:
import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold
In [77]:
score_cv = np.array([])
predd = np.zeros((len(test),))
In [78]:
sk = StratifiedKFold(5,random_state=0)
In [79]:
model_lgb = lgb.LGBMClassifier(max_bin=8,num_leaves=7,n_estimators=20000,learning_rate=0.004,random_state=0,early_stopping_rounds = 5000,verbose_eval=100)
In [80]:
for tr_i , te_i in sk.split(X_t,train.strat):
X_train = X_t.iloc[tr_i,:]
X_test = X_t.iloc[te_i,:]
y_train = y_t[tr_i]
y_test = y_t[te_i]
model_lgb.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test,y_test)])
score_cv = np.append(score_cv,model_lgb.best_score_)
y_predd = model_lgb.predict(test.drop("uniqueid",axis=1).astype("category"))
predd = np.add(predd,pd.Series(y_predd).apply(lambda x : 1.0 if x=="Yes" else 0.0))
/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.
% (min_groups, self.n_splits)), Warning)
/opt/conda/lib/python3.6/site-packages/lightgbm/engine.py:123: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
[1] training's binary_logloss: 0.397108 valid_1's binary_logloss: 0.435279 Training until validation scores don't improve for 5000 rounds. [2] training's binary_logloss: 0.396245 valid_1's binary_logloss: 0.434475 [3] training's binary_logloss: 0.395396 valid_1's binary_logloss: 0.433687 [4] training's binary_logloss: 0.394554 valid_1's binary_logloss: 0.432805 [5] training's binary_logloss: 0.393726 valid_1's binary_logloss: 0.431943 [6] training's binary_logloss: 0.392913 valid_1's binary_logloss: 0.431151 [7] training's binary_logloss: 0.392109 valid_1's binary_logloss: 0.430414 [8] training's binary_logloss: 0.39132 valid_1's binary_logloss: 0.42965 [9] training's binary_logloss: 0.390543 valid_1's binary_logloss: 0.428851 [10] training's binary_logloss: 0.389778 valid_1's binary_logloss: 0.428118 [11] training's binary_logloss: 0.389015 valid_1's binary_logloss: 0.427426 [12] training's binary_logloss: 0.388268 valid_1's binary_logloss: 0.426704 [13] training's binary_logloss: 0.387532 valid_1's binary_logloss: 0.425995 [14] training's binary_logloss: 0.386798 valid_1's binary_logloss: 0.425334 [15] training's 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binary_logloss: 0.256035 [7445] training's binary_logloss: 0.270357 valid_1's binary_logloss: 0.256033 [7446] training's binary_logloss: 0.270354 valid_1's binary_logloss: 0.256034 [7447] training's binary_logloss: 0.270353 valid_1's binary_logloss: 0.256033 [7448] training's binary_logloss: 0.27035 valid_1's binary_logloss: 0.256034 [7449] training's binary_logloss: 0.270348 valid_1's binary_logloss: 0.256034 [7450] training's binary_logloss: 0.270346 valid_1's binary_logloss: 0.256034 [7451] training's binary_logloss: 0.270343 valid_1's binary_logloss: 0.256033 [7452] training's binary_logloss: 0.270342 valid_1's binary_logloss: 0.256033 [7453] training's binary_logloss: 0.270339 valid_1's binary_logloss: 0.256035 [7454] training's binary_logloss: 0.270337 valid_1's binary_logloss: 0.256036 [7455] training's binary_logloss: 0.270335 valid_1's binary_logloss: 0.256036 [7456] training's binary_logloss: 0.270333 valid_1's binary_logloss: 0.256035 [7457] training's binary_logloss: 0.270329 valid_1's binary_logloss: 0.256035 [7458] training's binary_logloss: 0.270327 valid_1's binary_logloss: 0.256035 [7459] training's binary_logloss: 0.270326 valid_1's binary_logloss: 0.256035 [7460] training's binary_logloss: 0.270324 valid_1's binary_logloss: 0.256041 [7461] training's binary_logloss: 0.270323 valid_1's binary_logloss: 0.25604 [7462] training's binary_logloss: 0.270321 valid_1's binary_logloss: 0.256039 [7463] training's binary_logloss: 0.270319 valid_1's binary_logloss: 0.256039 [7464] training's binary_logloss: 0.270317 valid_1's binary_logloss: 0.256041 [7465] training's binary_logloss: 0.270315 valid_1's binary_logloss: 0.25604 [7466] training's binary_logloss: 0.270314 valid_1's binary_logloss: 0.25604 [7467] training's binary_logloss: 0.270312 valid_1's binary_logloss: 0.25604 [7468] training's binary_logloss: 0.270311 valid_1's binary_logloss: 0.256041 [7469] training's binary_logloss: 0.270309 valid_1's binary_logloss: 0.256044 [7470] training's binary_logloss: 0.270307 valid_1's binary_logloss: 0.256042 [7471] training's binary_logloss: 0.270306 valid_1's binary_logloss: 0.256048 [7472] training's binary_logloss: 0.270303 valid_1's binary_logloss: 0.256048 [7473] training's binary_logloss: 0.270302 valid_1's binary_logloss: 0.256048 [7474] training's binary_logloss: 0.270301 valid_1's binary_logloss: 0.256047 [7475] training's binary_logloss: 0.270298 valid_1's binary_logloss: 0.25605 [7476] training's binary_logloss: 0.270294 valid_1's binary_logloss: 0.25605 [7477] training's binary_logloss: 0.270293 valid_1's binary_logloss: 0.25605 [7478] training's binary_logloss: 0.270291 valid_1's binary_logloss: 0.256054 [7479] training's binary_logloss: 0.270289 valid_1's binary_logloss: 0.25606 [7480] training's binary_logloss: 0.270288 valid_1's binary_logloss: 0.25606 [7481] training's binary_logloss: 0.270285 valid_1's binary_logloss: 0.256064 Early stopping, best iteration is: [2481] training's binary_logloss: 0.281758 valid_1's binary_logloss: 0.25247
In [81]:
score_cv
Out[81]:
array([defaultdict(<class 'dict'>, {'training': {'binary_logloss': 0.2515882999834974}, 'valid_1': {'binary_logloss': 0.33763594270072966}}),
defaultdict(<class 'dict'>, {'training': {'binary_logloss': 0.25643864841619096}, 'valid_1': {'binary_logloss': 0.2989706712885212}}),
defaultdict(<class 'dict'>, {'training': {'binary_logloss': 0.2603805294610638}, 'valid_1': {'binary_logloss': 0.2745072607636881}}),
defaultdict(<class 'dict'>, {'training': {'binary_logloss': 0.2670918265194505}, 'valid_1': {'binary_logloss': 0.24771119929401345}}),
defaultdict(<class 'dict'>, {'training': {'binary_logloss': 0.2817582933314342}, 'valid_1': {'binary_logloss': 0.2524704473020787}})],
dtype=object)
In [ ]:
model_lgb.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test,y_test)])
In [ ]:
In [ ]:
In [ ]:
from catboost import CatBoostClassifier
ct = CatBoostClassifier(random_state=0,iterations=40000,learning_rate=0.001,verbose=200,early_stopping_rounds=1200
,cat_features=X_train.columns,max_ctr_complexity=3)
ct.fit(X_train,pd.Series(y_train).apply(lambda x : 1.0 if x=="Yes" else 0.0),eval_set=(X_test,pd.Series(y_test).apply(lambda x : 1.0 if x=="Yes" else 0.0)),use_best_model=True)
In [ ]:
y_pred = ct.predict(X_test)
print("accuracy : "+str(accuracy_score(pd.Series(y_test).apply(lambda x : 1.0 if x=="Yes" else 0.0),y_pred)))
print("precision : "+str(precision_score(pd.Series(y_test).apply(lambda x : 1.0 if x=="Yes" else 0.0),y_pred,pos_label=1)))
print("recall : "+str(recall_score(pd.Series(y_test).apply(lambda x : 1.0 if x=="Yes" else 0.0),y_pred,pos_label=1)))
In [ ]:
y_pred = model_lgb.predict(X_test)
print("accuracy : "+str(accuracy_score(y_test,y_pred)))
print("precision : "+str(precision_score(y_test,y_pred,pos_label="Yes")))
print("recall : "+str(recall_score(y_test,y_pred,pos_label="Yes")))
In [ ]:
id_test = test.uniqueid
In [ ]:
y_pred_test = model_lgb.predict(test.drop("uniqueid",axis=1).astype("category"))
In [ ]:
y_pred_test = ct.predict(test.drop("uniqueid",axis=1).astype("category"))
In [ ]:
y_pred_proba = ct.predict_proba(test.drop("uniqueid",axis=1).astype("category"))
In [ ]:
y_pred_test = pd.Series(y_pred_proba[:,1]).apply(lambda x : 1.0 if x>0.52 else 0)
In [ ]:
In [ ]:
y_pred_test.value_counts()
In [ ]:
df_fin = pd.DataFrame({"uniqueid":id_test+" x "+test.country
,"bank_account":pd.Series(y_pred_test).apply(lambda x : 1.0 if x=="Yes" else 0.0)})
df_fin.to_csv("submission.csv",index=False)
In [ ]:
df_fin = pd.DataFrame({"uniqueid":id_test+" x "+test.country
,"bank_account":y_pred_test})
df_fin.to_csv("submission.csv",index=False)
In [ ]:
df_fin = pd.DataFrame({"uniqueid":id_test+" x "+test.country
,"bank_account":pd.Series(predd).apply(lambda x : 1.0 if x>3.0 else 0.0)})
df_fin.to_csv("submission.csv",index=False)
In [ ]:
from IPython.display import FileLink
FileLink("submission.csv")